{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import metrics"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取 MNIST 数据，预处理"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = torchvision.datasets.MNIST(\n",
    "            root = './mnist/',\n",
    "            train = True,\n",
    "            transform = torchvision.transforms.ToTensor(),\n",
    "            download = True\n",
    "            )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将训练集数据转化为numpy格式，训练集60000张图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/datasets/mnist.py:75: UserWarning: train_data has been renamed data\n",
      "  warnings.warn(\"train_data has been renamed data\")\n",
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/datasets/mnist.py:65: UserWarning: train_labels has been renamed targets\n",
      "  warnings.warn(\"train_labels has been renamed targets\")\n"
     ]
    }
   ],
   "source": [
    "train_data = training_data.train_data.numpy()[:60000]\n",
    "train_label = training_data.train_labels.numpy()[:60000]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对图像数据进行归一化处理，缩放到[0,1]范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data / 255.0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集图片数量大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集标签数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_data[0])\n",
    "plt.title(train_label[0])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data = torchvision.datasets.MNIST(\n",
    "            root = './mnist/',\n",
    "            train = False,\n",
    "            transform = torchvision.transforms.ToTensor(),\n",
    "            download = True\n",
    "            )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将测试集数据转化为numpy格式，测试集10000张图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/datasets/mnist.py:80: UserWarning: test_data has been renamed data\n",
      "  warnings.warn(\"test_data has been renamed data\")\n",
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/datasets/mnist.py:70: UserWarning: test_labels has been renamed targets\n",
      "  warnings.warn(\"test_labels has been renamed targets\")\n"
     ]
    }
   ],
   "source": [
    "test_data = testing_data.test_data.numpy()[:10000]\n",
    "test_label = testing_data.test_labels.numpy()[:10000]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对图像数据进行归一化处理，缩放到[0,1]范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data / 255.0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试集图片数量大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试集标签数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGzCAYAAABpdMNsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAca0lEQVR4nO3df3TU9Z3v8dcAyQiYDIaQTCIBA/6gCqSnFNIsSqNkgbiHA8puQb094HHhQoNHSK1uPArS9ty0eBZdXYr/tFD3GKDsFbiyvXgkmnBoE1xQlqXWHJLGAiUJmrtkQpAQyGf/4Do6koDfMJN3Mjwf53zPITPfT+bt19En35nJNz7nnBMAAL1sgPUAAIDrEwECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEBAL1i0aJF8Pl+321/+8hfrEYFe5+NacEDsVVVVqa6uLuI255yWLl2qW265RX/4wx+MJgPsDLIeALge5OXlKS8vL+K2ffv26ezZs3rkkUeMpgJs8RIcYKSsrEw+n08PP/yw9SiACV6CAwx0dHQoIyND48aN0759+6zHAUxwBgQYeOutt9Tc3MzLb7iuESDAQFlZmRISEvS9733PehTADC/BAb3szJkzSk9P13333ac333zTehzADGdAQC/bsWMHn34DxBkQ0OsKCwu1b98+NTU1aciQIdbjAGY4AwJ60SeffKI9e/bogQceID647hEgoBdt3bpVFy5c4OU3QLwEB/SqvLw8/elPf9LJkyc1cOBA63EAUwQIAGCCl+AAACYIEADABAECAJggQAAAEwQIAGCCAAEATPS534ja2dmpkydPKikpST6fz3ocAIBHzjm1trYqMzNTAwZ0f57T5wJ08uRJZWVlWY8BALhGx48f18iRI7u9v88FKCkpSZJ0t+7XICUYTwMA8OqCOrRPvw3//7w7MQvQ+vXr9cILL6ixsVE5OTl65ZVXNGXKlKuu+/xlt0FK0CAfAQKAfuf/X1/nam+jxORDCFu3blVxcbFWr16t999/Xzk5OZo5c6ZOnToVi4cDAPRDMQnQunXrtHjxYj366KO688479eqrr2rIkCH61a9+FYuHAwD0Q1EP0Pnz53Xw4EEVFBR88SADBqigoEBVVVWX7d/e3q5QKBSxAQDiX9QD9Omnn+rixYtKT0+PuD09PV2NjY2X7V9aWqpAIBDe+AQcAFwfzH8QtaSkRC0tLeHt+PHj1iMBAHpB1D8Fl5qaqoEDB6qpqSni9qamJgWDwcv29/v98vv90R4DANDHRf0MKDExUZMmTVJ5eXn4ts7OTpWXlysvLy/aDwcA6Kdi8nNAxcXFWrhwob797W9rypQpeumll9TW1qZHH300Fg8HAOiHYhKg+fPn65NPPtGqVavU2Niob37zm9q9e/dlH0wAAFy/fM45Zz3El4VCIQUCAeVrDldCAIB+6ILrUIV2qqWlRcnJyd3uZ/4pOADA9YkAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJiIeoCef/55+Xy+iG3cuHHRfhgAQD83KBbf9K677tKePXu+eJBBMXkYAEA/FpMyDBo0SMFgMBbfGgAQJ2LyHtDRo0eVmZmpMWPG6JFHHtGxY8e63be9vV2hUChiAwDEv6gHKDc3V5s2bdLu3bu1YcMG1dfX65577lFra2uX+5eWlioQCIS3rKysaI8EAOiDfM45F8sHOH36tEaPHq1169bpscceu+z+9vZ2tbe3h78OhULKyspSvuZokC8hlqMBAGLggutQhXaqpaVFycnJ3e4X808HDBs2TLfffrtqa2u7vN/v98vv98d6DABAHxPznwM6c+aM6urqlJGREeuHAgD0I1EP0JNPPqnKykp9/PHH+v3vf68HHnhAAwcO1EMPPRTthwIA9GNRfwnuxIkTeuihh9Tc3KwRI0bo7rvvVnV1tUaMGBHthwIA9GNRD9CWLVui/S0BAHGIa8EBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACZi/gvp0LuaF+d5XjPq+13/ssCr+ehUuuc159u9/5bbmzd7XzPkxBnPaySp89CHPVoHwDvOgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCq2HHmad+VOZ5zbyh/9WzBxvbs2We5Xtf8vGFsz16qH/65N4erUPvee/UaM9rhv5joEePNaj8YI/W4evhDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHFSOPMy88s8Lxm1cSe/T3kpj86z2v+6xs+z2sSJ572vGbt+Dc8r5GkFzP2e17zb2dv9Lzmb4ac8bymN33mzntes799qOc1+Td0eF6jHvw7unX+//T+OJJuL+/RMnxNnAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACa4GGmcGfqv3i/UOPRfYzBIN5J76XFeCeb3aN1Pp97ieU1yZa3nNWvzb/W8pjcN+qzT85qhhxs8rxm+9397XjMhMcHzmiEfe1+D2OMMCABgggABAEx4DtDevXs1e/ZsZWZmyufzaceOHRH3O+e0atUqZWRkaPDgwSooKNDRo0ejNS8AIE54DlBbW5tycnK0fv36Lu9fu3atXn75Zb366qvav3+/hg4dqpkzZ+rcuXPXPCwAIH54/hBCYWGhCgsLu7zPOaeXXnpJzz77rObMmSNJeu2115Senq4dO3ZowQLvv60TABCfovoeUH19vRobG1VQUBC+LRAIKDc3V1VVVV2uaW9vVygUitgAAPEvqgFqbGyUJKWnp0fcnp6eHr7vq0pLSxUIBMJbVlZWNEcCAPRR5p+CKykpUUtLS3g7fvy49UgAgF4Q1QAFg0FJUlNTU8TtTU1N4fu+yu/3Kzk5OWIDAMS/qAYoOztbwWBQ5eXl4dtCoZD279+vvLy8aD4UAKCf8/wpuDNnzqi29otLj9TX1+vQoUNKSUnRqFGjtGLFCv30pz/VbbfdpuzsbD333HPKzMzU3Llzozk3AKCf8xygAwcO6N577w1/XVxcLElauHChNm3apKeeekptbW1asmSJTp8+rbvvvlu7d+/WDTfcEL2pAQD9ns8556yH+LJQKKRAIKB8zdEgHxcQBPqL5r/3/jJ71Zp/9rxm3f8b53nN3hljPa+RpAsNXX96F1d2wXWoQjvV0tJyxff1zT8FBwC4PhEgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMCE51/HACD+DRqd5XnNPz/j/crWCb6Bntds+6cCz2uGN1R5XoPY4wwIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADDBxUgBXOajlTd7XjPZ7/O85g/nP/O8JuXDs57XoG/iDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHFSIE41v43k3u07v2/fbEHq/yeVyx74gnPawb//j3Pa9A3cQYEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjgYqRAHDtW2LO/Y97o835h0Yfq/9rzmiG7/8PzGud5BfoqzoAAACYIEADAhOcA7d27V7Nnz1ZmZqZ8Pp927NgRcf+iRYvk8/kitlmzZkVrXgBAnPAcoLa2NuXk5Gj9+vXd7jNr1iw1NDSEt82bN1/TkACA+OP5QwiFhYUqLCy84j5+v1/BYLDHQwEA4l9M3gOqqKhQWlqa7rjjDi1btkzNzc3d7tve3q5QKBSxAQDiX9QDNGvWLL322msqLy/Xz3/+c1VWVqqwsFAXL17scv/S0lIFAoHwlpWVFe2RAAB9UNR/DmjBggXhP0+YMEETJ07U2LFjVVFRoenTp1+2f0lJiYqLi8Nfh0IhIgQA14GYfwx7zJgxSk1NVW1tbZf3+/1+JScnR2wAgPgX8wCdOHFCzc3NysjIiPVDAQD6Ec8vwZ05cybibKa+vl6HDh1SSkqKUlJStGbNGs2bN0/BYFB1dXV66qmndOutt2rmzJlRHRwA0L95DtCBAwd07733hr/+/P2bhQsXasOGDTp8+LB+/etf6/Tp08rMzNSMGTP0k5/8RH6/92tLAQDil+cA5efny7nuLwf41ltvXdNAALo2ICnJ85rv37OvR48V6jznec2p/zXG8xp/+797XoP4wbXgAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYCLqv5IbQGwcff4uz2t2pf6iR4815+g8z2v8v+XK1vCGMyAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQXIwUMtPyP73hec3j+y57X1F3o8LxGks78fKTnNX419OixcP3iDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHFSIFrNOjmTM9rVjy31fMav8/7f64L/uP7ntdI0oj/++89Wgd4wRkQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCi5ECX+Ib5P0/iZxdJzyv+bsbmz2veb01zfOa9Od69nfMzh6tArzhDAgAYIIAAQBMeApQaWmpJk+erKSkJKWlpWnu3LmqqamJ2OfcuXMqKirS8OHDdeONN2revHlqamqK6tAAgP7PU4AqKytVVFSk6upqvf322+ro6NCMGTPU1tYW3mflypV68803tW3bNlVWVurkyZN68MEHoz44AKB/8/SO6+7duyO+3rRpk9LS0nTw4EFNmzZNLS0t+uUvf6mysjLdd999kqSNGzfqG9/4hqqrq/Wd73wnepMDAPq1a3oPqKWlRZKUkpIiSTp48KA6OjpUUFAQ3mfcuHEaNWqUqqqquvwe7e3tCoVCERsAIP71OECdnZ1asWKFpk6dqvHjx0uSGhsblZiYqGHDhkXsm56ersbGxi6/T2lpqQKBQHjLysrq6UgAgH6kxwEqKirSkSNHtGXLlmsaoKSkRC0tLeHt+PHj1/T9AAD9Q49+EHX58uXatWuX9u7dq5EjR4ZvDwaDOn/+vE6fPh1xFtTU1KRgMNjl9/L7/fL7/T0ZAwDQj3k6A3LOafny5dq+fbveeecdZWdnR9w/adIkJSQkqLy8PHxbTU2Njh07pry8vOhMDACIC57OgIqKilRWVqadO3cqKSkp/L5OIBDQ4MGDFQgE9Nhjj6m4uFgpKSlKTk7W448/rry8PD4BBwCI4ClAGzZskCTl5+dH3L5x40YtWrRIkvTiiy9qwIABmjdvntrb2zVz5kz94he/iMqwAID44XPOOeshviwUCikQCChfczTIl2A9Dq4zvkl3eV7zb//nX2IwyeX+qqTI85phr3X94w9ALF1wHarQTrW0tCg5Obnb/bgWHADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEz06DeiAn3dwDtv79G6JVt2RnmSrt35K+9Xtr7lX6pjMAlghzMgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEFyNFXProBzf1aN3sIaEoT9K1kRXnvS9yLvqDAIY4AwIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATHAxUvR552ZP8bymfPY/9vDRhvRwHQCvOAMCAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAExwMVL0eSenDvS8ZtSg3ruo6OutaZ7XJITOe17jPK8A+jbOgAAAJggQAMCEpwCVlpZq8uTJSkpKUlpamubOnauampqIffLz8+Xz+SK2pUuXRnVoAED/5ylAlZWVKioqUnV1td5++211dHRoxowZamtri9hv8eLFamhoCG9r166N6tAAgP7P04cQdu/eHfH1pk2blJaWpoMHD2ratGnh24cMGaJgMBidCQEAcema3gNqaWmRJKWkpETc/vrrrys1NVXjx49XSUmJzp492+33aG9vVygUitgAAPGvxx/D7uzs1IoVKzR16lSNHz8+fPvDDz+s0aNHKzMzU4cPH9bTTz+tmpoavfHGG11+n9LSUq1Zs6anYwAA+qkeB6ioqEhHjhzRvn37Im5fsmRJ+M8TJkxQRkaGpk+frrq6Oo0dO/ay71NSUqLi4uLw16FQSFlZWT0dCwDQT/QoQMuXL9euXbu0d+9ejRw58or75ubmSpJqa2u7DJDf75ff7+/JGACAfsxTgJxzevzxx7V9+3ZVVFQoOzv7qmsOHTokScrIyOjRgACA+OQpQEVFRSorK9POnTuVlJSkxsZGSVIgENDgwYNVV1ensrIy3X///Ro+fLgOHz6slStXatq0aZo4cWJM/gEAAP2TpwBt2LBB0qUfNv2yjRs3atGiRUpMTNSePXv00ksvqa2tTVlZWZo3b56effbZqA0MAIgPnl+Cu5KsrCxVVlZe00AAgOsDV8MGvqS0+U7Pa6pm3uJ5jWv4T89rgHjDxUgBACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABNcjBR93ph/qPK85v5/+FYMJulOYy8+FhA/OAMCAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgos9dC845J0m6oA7JGQ8DAPDsgjokffH/8+70uQC1trZKkvbpt8aTAACuRWtrqwKBQLf3+9zVEtXLOjs7dfLkSSUlJcnn80XcFwqFlJWVpePHjys5OdloQnsch0s4DpdwHC7hOFzSF46Dc06tra3KzMzUgAHdv9PT586ABgwYoJEjR15xn+Tk5Ov6CfY5jsMlHIdLOA6XcBwusT4OVzrz+RwfQgAAmCBAAAAT/SpAfr9fq1evlt/vtx7FFMfhEo7DJRyHSzgOl/Sn49DnPoQAALg+9KszIABA/CBAAAATBAgAYIIAAQBMECAAgIl+E6D169frlltu0Q033KDc3Fy999571iP1uueff14+ny9iGzdunPVYMbd3717Nnj1bmZmZ8vl82rFjR8T9zjmtWrVKGRkZGjx4sAoKCnT06FGbYWPoasdh0aJFlz0/Zs2aZTNsjJSWlmry5MlKSkpSWlqa5s6dq5qamoh9zp07p6KiIg0fPlw33nij5s2bp6amJqOJY+PrHIf8/PzLng9Lly41mrhr/SJAW7duVXFxsVavXq33339fOTk5mjlzpk6dOmU9Wq+766671NDQEN727dtnPVLMtbW1KScnR+vXr+/y/rVr1+rll1/Wq6++qv3792vo0KGaOXOmzp0718uTxtbVjoMkzZo1K+L5sXnz5l6cMPYqKytVVFSk6upqvf322+ro6NCMGTPU1tYW3mflypV68803tW3bNlVWVurkyZN68MEHDaeOvq9zHCRp8eLFEc+HtWvXGk3cDdcPTJkyxRUVFYW/vnjxosvMzHSlpaWGU/W+1atXu5ycHOsxTEly27dvD3/d2dnpgsGge+GFF8K3nT592vn9frd582aDCXvHV4+Dc84tXLjQzZkzx2QeK6dOnXKSXGVlpXPu0r/7hIQEt23btvA+f/zjH50kV1VVZTVmzH31ODjn3He/+133xBNP2A31NfT5M6Dz58/r4MGDKigoCN82YMAAFRQUqKqqynAyG0ePHlVmZqbGjBmjRx55RMeOHbMeyVR9fb0aGxsjnh+BQEC5ubnX5fOjoqJCaWlpuuOOO7Rs2TI1NzdbjxRTLS0tkqSUlBRJ0sGDB9XR0RHxfBg3bpxGjRoV18+Hrx6Hz73++utKTU3V+PHjVVJSorNnz1qM160+dzXsr/r000918eJFpaenR9yenp6ujz76yGgqG7m5udq0aZPuuOMONTQ0aM2aNbrnnnt05MgRJSUlWY9norGxUZK6fH58ft/1YtasWXrwwQeVnZ2turo6PfPMMyosLFRVVZUGDhxoPV7UdXZ2asWKFZo6darGjx8v6dLzITExUcOGDYvYN56fD10dB0l6+OGHNXr0aGVmZurw4cN6+umnVVNTozfeeMNw2kh9PkD4QmFhYfjPEydOVG5urkaPHq3f/OY3euyxxwwnQ1+wYMGC8J8nTJigiRMnauzYsaqoqND06dMNJ4uNoqIiHTly5Lp4H/RKujsOS5YsCf95woQJysjI0PTp01VXV6exY8f29phd6vMvwaWmpmrgwIGXfYqlqalJwWDQaKq+YdiwYbr99ttVW1trPYqZz58DPD8uN2bMGKWmpsbl82P58uXatWuX3n333YjfHxYMBnX+/HmdPn06Yv94fT50dxy6kpubK0l96vnQ5wOUmJioSZMmqby8PHxbZ2enysvLlZeXZziZvTNnzqiurk4ZGRnWo5jJzs5WMBiMeH6EQiHt37//un9+nDhxQs3NzXH1/HDOafny5dq+fbveeecdZWdnR9w/adIkJSQkRDwfampqdOzYsbh6PlztOHTl0KFDktS3ng/Wn4L4OrZs2eL8fr/btGmT+/DDD92SJUvcsGHDXGNjo/VoveqHP/yhq6iocPX19e53v/udKygocKmpqe7UqVPWo8VUa2ur++CDD9wHH3zgJLl169a5Dz74wP35z392zjn3s5/9zA0bNszt3LnTHT582M2ZM8dlZ2e7zz77zHjy6LrScWhtbXVPPvmkq6qqcvX19W7Pnj3uW9/6lrvtttvcuXPnrEePmmXLlrlAIOAqKipcQ0NDeDt79mx4n6VLl7pRo0a5d955xx04cMDl5eW5vLw8w6mj72rHoba21v34xz92Bw4ccPX19W7nzp1uzJgxbtq0acaTR+oXAXLOuVdeecWNGjXKJSYmuilTprjq6mrrkXrd/PnzXUZGhktMTHQ333yzmz9/vqutrbUeK+beffddJ+mybeHChc65Sx/Ffu6551x6errz+/1u+vTprqamxnboGLjScTh79qybMWOGGzFihEtISHCjR492ixcvjru/pHX1zy/Jbdy4MbzPZ5995n7wgx+4m266yQ0ZMsQ98MADrqGhwW7oGLjacTh27JibNm2aS0lJcX6/3916663uRz/6kWtpabEd/Cv4fUAAABN9/j0gAEB8IkAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYOK/ARDwzMLvaVDnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_data[0])\n",
    "plt.title(test_label[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = train_data.reshape(60000, -1)\n",
    "test_features = test_data.reshape(10000, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_model(test_label, test_result):\n",
    "    precision = metrics.precision_score(test_label, test_result, average='micro')\n",
    "    print('采用micro平均的precision: ', precision)\n",
    "\n",
    "    recall = metrics.recall_score(test_label, test_result, average='micro')\n",
    "    print('采用micro平均的recall: ', recall)\n",
    "\n",
    "    f1 = metrics.f1_score(test_label, test_result, average='micro')\n",
    "    print('采用micro平均的f1: ', f1)\n",
    "\n",
    "    other = metrics.classification_report(test_label, test_result)\n",
    "    print('其他评估: ')\n",
    "    print(other)\n",
    "    \n",
    "    # 混淆矩阵\n",
    "    print('混淆矩阵: ')\n",
    "    matrix = metrics.confusion_matrix(test_label, test_result)\n",
    "    sns.heatmap(matrix, annot=True, cmap='Blues', fmt='g')\n",
    "    plt.rcParams['font.sans-serif']=['SimHei'] \n",
    "    plt.rcParams['axes.unicode_minus']=False\n",
    "    plt.xlabel('predict')\n",
    "    plt.ylabel('label')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建支持向量机模型"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 线性核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_linear = svm.LinearSVC(C=5, max_iter=1000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=5)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_linear.fit(train_features, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result = clf_linear.predict(test_features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型性能评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "采用micro平均的precision:  0.9139\n",
      "采用micro平均的recall:  0.9139\n",
      "采用micro平均的f1:  0.9139\n",
      "其他评估: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96       980\n",
      "           1       0.95      0.98      0.97      1135\n",
      "           2       0.91      0.90      0.90      1032\n",
      "           3       0.93      0.88      0.90      1010\n",
      "           4       0.92      0.92      0.92       982\n",
      "           5       0.85      0.89      0.87       892\n",
      "           6       0.93      0.95      0.94       958\n",
      "           7       0.95      0.89      0.92      1028\n",
      "           8       0.87      0.85      0.86       974\n",
      "           9       0.89      0.89      0.89      1009\n",
      "\n",
      "    accuracy                           0.91     10000\n",
      "   macro avg       0.91      0.91      0.91     10000\n",
      "weighted avg       0.91      0.91      0.91     10000\n",
      "\n",
      "混淆矩阵: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_model(test_label, test_result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 多项式核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_poly = svm.SVC(C=5, kernel='poly', gamma=0.05, max_iter=1000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/sklearn/svm/_base.py:289: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=5, gamma=0.05, kernel='poly', max_iter=1000)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_poly.fit(train_features, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result = clf_poly.predict(test_features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型性能评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "采用micro平均的precision:  0.9789\n",
      "采用micro平均的recall:  0.9789\n",
      "采用micro平均的f1:  0.9789\n",
      "其他评估: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98       980\n",
      "           1       0.98      0.99      0.99      1135\n",
      "           2       0.98      0.97      0.98      1032\n",
      "           3       0.98      0.98      0.98      1010\n",
      "           4       0.98      0.98      0.98       982\n",
      "           5       0.98      0.97      0.97       892\n",
      "           6       0.98      0.98      0.98       958\n",
      "           7       0.98      0.97      0.98      1028\n",
      "           8       0.98      0.97      0.98       974\n",
      "           9       0.98      0.97      0.97      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n",
      "混淆矩阵: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_model(test_label, test_result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 径向基核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rbf = svm.SVC(C=5, kernel='rbf', gamma=0.05, max_iter=1000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/sklearn/svm/_base.py:289: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=5, gamma=0.05, max_iter=1000)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_rbf.fit(train_features, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result = clf_rbf.predict(test_features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型性能评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "采用micro平均的precision:  0.9833\n",
      "采用micro平均的recall:  0.9833\n",
      "采用micro平均的f1:  0.9833\n",
      "其他评估: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.98      0.98      0.98      1032\n",
      "           3       0.98      0.98      0.98      1010\n",
      "           4       0.99      0.98      0.99       982\n",
      "           5       0.98      0.98      0.98       892\n",
      "           6       0.99      0.99      0.99       958\n",
      "           7       0.98      0.98      0.98      1028\n",
      "           8       0.97      0.99      0.98       974\n",
      "           9       0.98      0.96      0.97      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n",
      "混淆矩阵: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_model(test_label, test_result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### sigmoid核函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_sigmoid = svm.SVC(C=5, kernel='sigmoid', gamma=0.05, max_iter=1000)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jjn/anaconda3/envs/pytorch/lib/python3.7/site-packages/sklearn/svm/_base.py:289: ConvergenceWarning: Solver terminated early (max_iter=1000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  ConvergenceWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=5, gamma=0.05, kernel='sigmoid', max_iter=1000)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_sigmoid.fit(train_features, train_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_result = clf_sigmoid.predict(test_features)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型性能评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "采用micro平均的precision:  0.253\n",
      "采用micro平均的recall:  0.253\n",
      "采用micro平均的f1:  0.253\n",
      "其他评估: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.61      0.61       980\n",
      "           1       0.11      0.22      0.15      1135\n",
      "           2       0.34      0.17      0.23      1032\n",
      "           3       0.45      0.24      0.32      1010\n",
      "           4       0.47      0.26      0.33       982\n",
      "           5       0.17      0.06      0.09       892\n",
      "           6       0.64      0.22      0.33       958\n",
      "           7       0.82      0.22      0.35      1028\n",
      "           8       0.07      0.09      0.08       974\n",
      "           9       0.14      0.42      0.21      1009\n",
      "\n",
      "    accuracy                           0.25     10000\n",
      "   macro avg       0.38      0.25      0.27     10000\n",
      "weighted avg       0.38      0.25      0.27     10000\n",
      "\n",
      "混淆矩阵: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_model(test_label, test_result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结论：\n",
    "迭代次数较小时，如10，使用线性核训练时，就可以得到很好的结果，而其他核函数结果不好。\n",
    "迭代次数增大到1000时，线性核、多项式核、径向基核准确率都超过了90%，多项式核、径向基核优于线性核。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
